Comparative Analysis of the Features of a 5G Network Production Dataset: The Machine Learning Approach

Abstract

5G networks deployment is much data driven, leading to more energy consumption. The need to efficiently manage this energy consumption is a major drive in the comparative analysis of the features of a 5G production dataset. The features of the 5G production dataset generated with G-Net track pro were analyzed using Python programming language. From the correlation coefficient results obtained, the highest correlation value of 0.78 exists between the reference signal power and the received signal reference power of the neighbouring cells. Using the significant indicator, we observed that the signal to noise ratio is the most important of all the features. Using heat map and scatter plots, we further observed that there were good relationships between the key features selected from the significant indicator. These features will play a big role in improving the energy efficiency of a 5G network.